code: def forward(self, hs_pad, hlens, ys_in_pad, ys_in_lens, strm_idx=0):
      # to support mutiple encoder asr mode, in single encoder mode,
      # convert torch.Tensor to List of torch.Tensor
      if self.num_encs == 1:
          hs_pad = [hs_pad]
          hlens = [hlens]
  
      # attention index for the attention module
      # in SPA (speaker parallel attention),
      # att_idx is used to select attention module. In other cases, it is 0.
      att_idx = min(strm_idx, len(self.att_list) - 1)
  
      # hlens should be list of list of integer
      hlens = [
          list(map(int, hlens[idx])) for idx in range(self.num_encs)
      ]
  
      # get dim, length info
      olength = ys_in_pad.size(1)
  
      # initialization
      c_list = [self.zero_state(hs_pad[0])]
      z_list = [self.zero_state(hs_pad[0])]
      for _ in range(1, self.dlayers):
          c_list.append(self.zero_state(hs_pad[0]))
          z_list.append(self.zero_state(hs_pad[0]))
      z_all = []
      if self.num_encs == 1:
          att_w = None
          self.att_list[att_idx].reset()  # reset pre-computation of h
      else:
          att_w_list = [None] * (self.num_encs + 1)  # atts + han
          att_c_list = [None] * self.num_encs  # atts
          for idx in range(self.num_encs + 1):
              # reset pre-computation of h in atts and han
              self.att_list[idx].reset()
  
      # pre-computation of embedding
      eys = self.dropout_emb(self.embed(ys_in_pad))  # utt x olen x zdim
  
      # loop for an output sequence
      for i in range(olength):
          if self.num_encs == 1:
              att_c, att_w = self.att_list[att_idx](
                  hs_pad[0],
                  hlens[0],
                  self.dropout_dec[0](z_list[0]),
                  att_w,
              )
          else:
              for idx in range(self.num_encs):
                  att_c_list[idx], att_w_list[idx] = self.att_list[idx](
                      hs_pad[idx],
                      hlens[idx],
                      self.dropout_dec[0](z_list[0]),
                      att_w_list[idx],
                  )
              hs_pad_han = torch.stack(att_c_list, dim=1)
              hlens_han = [self.num_encs] * len(ys_in_pad)
              att_c, att_w_list[self.num_encs] = self.att_list[
                  self.num_encs
              ](
                  hs_pad_han,
                  hlens_han,
                  self.dropout_dec[0](z_list[0]),
                  att_w_list[self.num_encs],
              )
          if i > 0 and random.random() < self.sampling_probability:
              z_out = self.output(z_all[-1])
              z_out = np.argmax(z_out.detach().cpu(), axis=1)
              z_out = self.dropout_emb(
                  self.embed(to_device(self, z_out))
              )
              ey = torch.cat(
                  (z_out, att_c), dim=1
              )  # utt x (zdim + hdim)
          else:
              # utt x (zdim + hdim)
              ey = torch.cat((eys[:, i, :], att_c), dim=1)
          z_list, c_list = self.rnn_forward(
              ey, z_list, c_list, z_list, c_list
          )
          if self.context_residual:
              z_all.append(
                  torch.cat(
                      (self.dropout_dec[-1](z_list[-1]), att_c), dim=-1
                  )
              )  # utt x (zdim + hdim)
          else:
              z_all.append(
                  self.dropout_dec[-1](z_list[-1])
              )  # utt x (zdim)
  
      z_all = torch.stack(z_all, dim=1)
      z_all = self.output(z_all)
      z_all.masked_fill_(
          make_pad_mask(ys_in_lens, z_all, 1),
          0,
      )
      return z_all, ys_in_lens
  
file_path: espnet/espnet2/asr/decoder/rnn_decoder.py
imports: import random
  import numpy
  import torch
  import torch.nn.functional
  from typeguard import check_argument_types
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet2.utils.get_default_kwargs import get_default_kwargs
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask, to_device
  from espnet.nets.pytorch_backend.rnn.attentions import initial_att
label_desc: Use image node_id 4 for calling the RNNDecoder obj's underlying member method code with example usage: obj.forward(hs_pad, hlens, ys_in_pad, ys_in_lens, strm_idx) and returns: z_all,  ys_in_lens